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DAHA ÇOK ÇALIŞ 🍀🍀

📚 STAT 100 — Chapters 1–3 Flash Cards FLASH CARD 1 Front: What is an estimate? Back: A number or percentage used to describe something about a population. Example: 75% of students play a sport → 75% = estimate FLASH CARD 2 Front: What is the population of interest? Back: The whole group of people or things you want information about. Example: 75% of high school students play a sport → high school students = population. FLASH CARD 3 Front: What is an attribute? Back: A characteristic or piece of information being measured about an observation. Example: Name, major, GPA, age. FLASH CARD 4 Front: What is an observation? Back: One person, object, place, or event in a dataset. ⭐ Usually, one row = one observation. FLASH CARD 5 Front: In a tidy data table, what does each ROW represent? Back: One observation. FLASH CARD 6 Front: In a tidy data table, what does each COLUMN represent? Back: One attribute. FLASH CARD 7 Front: What is quantitative data? Back: Data that represents a number or amount. Examples: GPA, age, price, height, wind speed. FLASH CARD 8 Front: What is categorical data? Back: Data that places something into a group or category. Examples: major, eye color, blood type, ocean. FLASH CARD 9 Front: Is blood type quantitative or categorical? Back: Categorical. A, B, AB, and O are categories, not amounts. FLASH CARD 10 Front: What is a rating scale? Back: Categories that have a meaningful order. Example: Poor → Fair → Good → Excellent. FLASH CARD 11 Front: What is text data? Back: Written words or responses. Example: A student writing why they enjoyed a class. FLASH CARD 12 Front: What is time-series data? Back: Data collected or measured over time. Example: Google’s stock price each day. FLASH CARD 13 Front: What is a measurement? Back: The way an attribute is measured or collected. FLASH CARD 14 Front: What is reliability? Back: How much you can trust a measurement to give dependable information. FLASH CARD 15 Front: What is data validation? Back: Rules used to prevent invalid data from being entered. Example: If GPA must be 0–4, don’t allow someone to enter 40. FLASH CARD 16 Front: What is data cleaning? Back: Finding and handling invalid or incorrect values AFTER data has been collected. FLASH CARD 17 Front: Validation vs. data cleaning? Back: Validation = PREVENT bad data. Cleaning = deal with bad data afterward. FLASH CARD 18 Front: What is a generalization claim? Back: Using information from a sample to make a claim about a larger population. FLASH CARD 19 Front: What is a causal claim? Back: A claim saying one thing causes another thing to change. FLASH CARD 20 Front: What is a causal factor? Back: The thing believed to cause the change. Example: “Eating breakfast improves grades.” → Eating breakfast = causal factor. FLASH CARD 21 Front: What is an outcome? Back: The thing that is affected or changed. Example: “Eating breakfast improves grades.” → Grades = outcome. ⭐ QUICK MEMORY CARD Front: What should I remember most for Chapters 1–3? Back: Estimate = NUMBER Population = WHO Attribute = WHAT Observation = ROW Attribute = COLUMN Quantitative = AMOUNT Categorical = GROUP Time series = OVER TIME Reliability = CAN I TRUST IT? Validation = PREVENT bad data Cleaning = FIX/REMOVE bad data afterward

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